{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "import scipy.io as sio\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn import model_selection\n",
    "import matplotlib.pyplot as plt \n",
    "from keras import layers\n",
    "#from keras.models import Sequential\n",
    "from keras.models import model_from_json\n",
    "from livelossplot.keras import PlotLossesCallback\n",
    "from IPython.display import clear_output\n",
    "from keras.utils.vis_utils import plot_model\n",
    "#from sklearn import preprocessing\n",
    "#import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'MarmousiModel2.mat'\n",
    "marmousi_cube = sio.loadmat(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carregar dados e cubos de Sísmica e Impedância"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "VP = marmousi_cube['Vp']\n",
    "#VS = marmousi_cube['Vs']\n",
    "#IS = marmousi_cube['IS']\n",
    "pimpedance = marmousi_cube['IP']\n",
    "seismic = marmousi_cube['seismic']\n",
    "\n",
    "WAVELET = marmousi_cube['wavelet']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_traces = pimpedance.shape[1]\n",
    "\n",
    "train_wells_loc = np.arange(0,n_traces,135)\n",
    "all_wells_loc = np.arange(n_traces)\n",
    "\n",
    "wells_loc = np.array(list(set(all_wells_loc) - set(train_wells_loc)))\n",
    "valid_wells_loc, unlabed_wells_loc = model_selection.train_test_split(wells_loc,\n",
    "                                                                      test_size=0.9,\n",
    "                                                                      train_size=0.1,\n",
    "                                                                      shuffle=True)\n",
    "BATCH_SIZE = 10\n",
    "noise_dim = 2800\n",
    "IMG_SHAPE = (2800,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizar Sismica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "seismic_norm = seismic.flatten()\n",
    "ymin = 0\n",
    "ymax = 1\n",
    "seismic_norm = (ymax-ymin)*(seismic_norm-np.min(seismic_norm))/(np.max(seismic_norm)-np.min(seismic_norm)) + ymin\n",
    "seismic_norm = seismic_norm.reshape(seismic.shape)\n",
    "#seismic_norm = seismic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pimpedance_norm = pimpedance.flatten()\n",
    "ymin = 0\n",
    "ymax = 1\n",
    "pimpedance_norm = (ymax-ymin)*(pimpedance_norm-np.min(pimpedance_norm))/(np.max(pimpedance_norm)-np.min(pimpedance_norm)) + ymin\n",
    "pimpedance = pimpedance_norm.reshape(pimpedance.shape)\n",
    "#seismic_norm = seismic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizar estatísticas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"data_info = pd.DataFrame(zip(seismic_norm.flatten(),\\n                             pimpedance.flatten()),\\n                         columns=['Seismic CL Stats','PImp CL Stats'])\\ndata_info.describe()\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''data_info = pd.DataFrame(zip(seismic_norm.flatten(),\n",
    "                             pimpedance.flatten()),\n",
    "                         columns=['Seismic CL Stats','PImp CL Stats'])\n",
    "data_info.describe()'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparar os Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.transpose(seismic_norm[:,train_wells_loc])\n",
    "Y_train = np.transpose(pimpedance[:-1,train_wells_loc])\n",
    "X_train = np.expand_dims(X_train,axis=(2))\n",
    "Y_train = np.expand_dims(Y_train,axis=(2))\n",
    "\n",
    "X_valid = np.transpose(seismic_norm[:,valid_wells_loc])\n",
    "Y_valid = np.transpose(pimpedance[:-1,valid_wells_loc])\n",
    "X_valid = np.expand_dims(X_valid,axis=(2))\n",
    "Y_valid = np.expand_dims(Y_valid,axis=(2))\n",
    "\n",
    "X_test = np.transpose(seismic_norm)\n",
    "Y_test = np.transpose(pimpedance[:-1,:])\n",
    "X_test = np.expand_dims(X_test,axis=(2))\n",
    "Y_test = np.expand_dims(Y_test,axis=(2))\n",
    "\n",
    "UNLABED_SEISMIC = np.transpose(seismic_norm[:,unlabed_wells_loc])\n",
    "UNLABED_SEISMIC = np.expand_dims(UNLABED_SEISMIC,axis=(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     IP (Y_train) shape:  (21, 700, 1)\n",
      "seismic (X_train) shape:  (21, 700, 1)\n",
      "     IP (Y_valid) shape:  (270, 700, 1)\n",
      "seismic (X_valid) shape:  (270, 700, 1)\n",
      "     IP (Y_test) shape:  (2721, 700, 1)\n",
      "seismic (X_test) shape:  (2721, 700, 1)\n"
     ]
    }
   ],
   "source": [
    "print('     IP (Y_train) shape: ',Y_train.shape)\n",
    "print('seismic (X_train) shape: ',X_train.shape)\n",
    "\n",
    "print('     IP (Y_valid) shape: ',Y_valid.shape)\n",
    "print('seismic (X_valid) shape: ',X_valid.shape)\n",
    "\n",
    "print('     IP (Y_test) shape: ',Y_test.shape)\n",
    "print('seismic (X_test) shape: ',X_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizar Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Output P-Impedance Test - Ground True')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, axes1 = plt.subplots(nrows=3, ncols=2, figsize=(15,15))\n",
    "\n",
    "axes1[0,0].imshow(seismic)\n",
    "axes1[0,0].set_title(\"2D crossline seismic\")\n",
    "\n",
    "axes1[0,1].imshow(pimpedance)\n",
    "axes1[0,1].set_title(\"2D crossline P-Impedance\")\n",
    "\n",
    "axes1[1,0].imshow(X_train[:,:,0])\n",
    "axes1[1,0].set_title(\"Input Seismic Train\")\n",
    "\n",
    "axes1[1,1].imshow(Y_train[:,:,0])\n",
    "axes1[1,1].set_title(\"Output P-Impedance Train\")\n",
    "\n",
    "axes1[2,0].imshow(X_test[:,:,0])\n",
    "axes1[2,0].set_title(\"Input Seismic Test\")\n",
    "axes1[2,1].imshow(Y_test[:,:,0])\n",
    "axes1[2,1].set_title(\"Output P-Impedance Test - Ground True\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criação e Configuração da Rede"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carregar Modelo Forward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded forward model from disk\n"
     ]
    }
   ],
   "source": [
    "# load json and create model\n",
    "json_file = open('forward_model/modelnorm_1d.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "forward_model = model_from_json(loaded_model_json)\n",
    "# load weights into new model\n",
    "forward_model.load_weights(\"forward_model/modelnorm_1d.h5\")\n",
    "print(\"Loaded forward model from disk\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_forward = False\n",
    "if test_forward:\n",
    "    predict = forward_model(Y_test[:8000,:,:])\n",
    "    predict = np.transpose(predict[:,:,0].numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1, 700, 2721)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if test_forward:\n",
    "    trace_id = np.random.randint(predict.shape[1])\n",
    "    plt.plot(predict[:,trace_id])\n",
    "    plt.plot(X_test[trace_id,:,0]+0.5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the generator\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def ResBlock(inputRB):\n",
    "    RB0 = layers.Conv1D(filters=16,kernel_size=300,padding='same')(inputRB)\n",
    "    RB = layers.BatchNormalization()(RB0)\n",
    "    RB = layers.ReLU()(RB)\n",
    "    RB = layers.Conv1D(filters=16,kernel_size=3,padding='same')(RB)\n",
    "    RB = layers.BatchNormalization()(RB)\n",
    "    RB = layers.Add()([RB,inputRB])\n",
    "    RB = layers.ReLU()(RB)\n",
    "    return (RB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_generator_model():\n",
    "    noise = layers.Input(shape=(noise_dim,1))\n",
    "\n",
    "    GEN = layers.Conv1D(filters=16,kernel_size=300,padding='same')(noise)\n",
    "    GEN = layers.BatchNormalization()(GEN)\n",
    "    GEN = layers.ReLU()(GEN)\n",
    "    \n",
    "    GEN = ResBlock(GEN)\n",
    "    GEN = ResBlock(GEN)\n",
    "    GEN = ResBlock(GEN)\n",
    "    \n",
    "    GEN = layers.Conv1D(filters=1,kernel_size=3,padding='same')(GEN)\n",
    "\n",
    "    GENmodel = keras.models.Model(inputs=noise,outputs=GEN)\n",
    "    return GENmodel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Create the discriminator (the critic in the original WGAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def PyramidPoolingModule(inputASSP):\n",
    "    r1 = layers.Conv1D(filters=32,kernel_size=3,padding='same',dilation_rate=1)(inputASSP)\n",
    "    r1 = layers.BatchNormalization()(r1)\n",
    "    r1 = layers.ReLU()(r1)\n",
    "    r2 = layers.Conv1D(filters=32,kernel_size=3,padding='same',dilation_rate=3)(inputASSP)\n",
    "    r2 = layers.BatchNormalization()(r2)\n",
    "    r2 = layers.ReLU()(r2)\n",
    "    r3 = layers.Conv1D(filters=32,kernel_size=3,padding='same',dilation_rate=5)(inputASSP)\n",
    "    r3 = layers.BatchNormalization()(r3)\n",
    "    r3 = layers.ReLU()(r3)\n",
    "    r4 = layers.Conv1D(filters=32,kernel_size=3,padding='same',dilation_rate=7)(inputASSP)\n",
    "    r4 = layers.BatchNormalization()(r4)\n",
    "    r4 = layers.ReLU()(r4)\n",
    "    assp = layers.concatenate([r1,r2,r3,r4])\n",
    "    assp = layers.Conv1D(filters=64,kernel_size=3,padding='same',strides=2)(assp)\n",
    "    return assp\n",
    "\n",
    "def EncoderBlock(inputEncB):\n",
    "    ENCN = layers.Conv1D(filters=16,kernel_size=300,padding='same')(inputEncB)\n",
    "    ENCN = layers.ReLU()(ENCN)\n",
    "    ENCN = layers.Conv1D(filters=16,kernel_size=3,padding='same')(ENCN)\n",
    "    ENCN = layers.Add()([ENCN, inputEncB])\n",
    "    ENCN = layers.MaxPooling1D(pool_size=1,strides=2)(ENCN)\n",
    "    ENCN = layers.ReLU()(ENCN)\n",
    "    return ENCN\n",
    "\n",
    "def Encoder(inputEncoder):\n",
    "    ENC = layers.Conv1D(filters=16,kernel_size=300,padding='same',strides=2)(inputEncoder)\n",
    "    ENC = layers.LeakyReLU()(ENC)\n",
    "    ENC = layers.MaxPooling1D(pool_size=1,strides=2)(ENC)\n",
    "    ENC = EncoderBlock(ENC)\n",
    "    ENC = EncoderBlock(ENC)\n",
    "    ENC = EncoderBlock(ENC)\n",
    "    return  ENC\n",
    "\n",
    "def get_discriminator_model():       \n",
    "        input_disc = layers.Input(shape=(700,1))        \n",
    "        enc_out = Encoder(input_disc)\n",
    "        assp_out = PyramidPoolingModule(enc_out)\n",
    "        fc_out = layers.Dense(256,kernel_initializer=tf.keras.initializers.HeNormal())(assp_out)\n",
    "        lkr_out = layers.LeakyReLU()(fc_out)\n",
    "        fc2_outFlatten = layers.Flatten()(lkr_out)\n",
    "        fc2_out = layers.Dense(1,\n",
    "                               kernel_initializer=tf.keras.initializers.HeNormal(),\n",
    "                               #activation=keras.activations.sigmoid\n",
    "                              )(fc2_outFlatten)\n",
    "        \n",
    "        DISCmodel = keras.models.Model(inputs=input_disc,outputs=fc2_out)\n",
    "        return DISCmodel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the WGAN-GP model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "little_amout = 1e-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WGAN(keras.Model):\n",
    "    def __init__(\n",
    "        self,\n",
    "        discriminator,\n",
    "        generator,\n",
    "        latent_dim,\n",
    "        discriminator_extra_steps=3,\n",
    "        gp_weight=10.0,\n",
    "        alpha_weight = 1000,\n",
    "        beta_weight = 500\n",
    "    ):\n",
    "        super(WGAN, self).__init__()\n",
    "        self.discriminator = discriminator\n",
    "        self.generator = generator\n",
    "        self.latent_dim = latent_dim\n",
    "        self.d_steps = discriminator_extra_steps\n",
    "        self.gp_weight = gp_weight\n",
    "        self.alpha_weight = alpha_weight\n",
    "        self.beta_weight = beta_weight\n",
    "\n",
    "    def compile(self, d_optimizer, g_optimizer, d_loss_fn, g_loss_fn, imp_loss_fn, seis_loss_fn):\n",
    "        super(WGAN, self).compile()\n",
    "        self.d_optimizer = d_optimizer\n",
    "        self.g_optimizer = g_optimizer\n",
    "        self.d_loss_fn = d_loss_fn\n",
    "        self.g_loss_fn = g_loss_fn\n",
    "        self.imp_loss_fn = imp_loss_fn\n",
    "        self.seis_loss_fn = seis_loss_fn\n",
    "\n",
    "    def gradient_penalty(self, batch_size, real_images, fake_images):\n",
    "        \n",
    "        # Get the interpolated image\n",
    "        alpha = tf.random.normal([batch_size, 1, 1], 0.0, 1.0)\n",
    "        diff = fake_images - real_images\n",
    "        interpolated = real_images + alpha * diff\n",
    "        with tf.GradientTape() as gp_tape:\n",
    "            gp_tape.watch(interpolated)\n",
    "            # 1. Get the discriminator output for this interpolated image.\n",
    "            pred = self.discriminator(interpolated, training=True)\n",
    "       \n",
    "        #2. Calculate the gradients w.r.t to this interpolated image.\n",
    "        grads = gp_tape.gradient(pred, [interpolated])[0]\n",
    "        #3. Calculate the norm of the gradients.\n",
    "        norm = tf.sqrt(tf.reduce_sum(tf.square(grads), axis=[1, 2]))\n",
    "        gp = tf.reduce_mean((norm - 1.0) ** 2)\n",
    "        return gp\n",
    "\n",
    "    def train_step(self, data):\n",
    "        noises_input,real_images = data\n",
    "        if isinstance(real_images, tuple):\n",
    "            real_images = real_images[0]\n",
    "\n",
    "        # Get the batch size\n",
    "        batch_size = tf.shape(real_images)[0]\n",
    "\n",
    "        random_latent_vectors = noises_input\n",
    "        \n",
    "        for i in range(self.d_steps):\n",
    "            # Get the latent vector\n",
    "\n",
    "            with tf.GradientTape() as tape:\n",
    "                \n",
    "                fake_images = self.generator(random_latent_vectors, training=True)\n",
    "                fake_logits = self.discriminator(fake_images, training=True)\n",
    "\n",
    "                real_logits = self.discriminator(real_images, training=True)\n",
    "\n",
    "                # discriminator loss\n",
    "                d_cost = self.d_loss_fn(real_img=real_logits, fake_img=fake_logits)\n",
    "                # gradient penalty:\n",
    "                gp = self.gradient_penalty(batch_size, real_images, fake_images)\n",
    "                \n",
    "                d_loss = d_cost + gp * self.gp_weight + little_amout\n",
    "\n",
    "            \n",
    "            d_gradient = tape.gradient(d_loss, self.discriminator.trainable_variables)\n",
    "            self.d_optimizer.apply_gradients(zip(d_gradient, self.discriminator.trainable_variables))\n",
    "        \n",
    "        #unlabed seismic mini batch:\n",
    "        unlabed_wells_loc = np.arange(UNLABED_SEISMIC.shape[1])\n",
    "        np.random.shuffle(unlabed_wells_loc)\n",
    "        unlabed_batch_idxs = unlabed_wells_loc[0:BATCH_SIZE]\n",
    "        unlabed_batch = UNLABED_SEISMIC[unlabed_batch_idxs,:,:]\n",
    "        \n",
    "        with tf.GradientTape() as tape:\n",
    "            \n",
    "            generated_images = self.generator(random_latent_vectors, training=True)\n",
    "            gen_img_logits = self.discriminator(generated_images, training=True)\n",
    "            \n",
    "            #Generator loss\n",
    "            g_cost = self.g_loss_fn(gen_img_logits)\n",
    "            \n",
    "            #Impedance loss\n",
    "            imp_loss = self.imp_loss_fn(generated_images, real_images)\n",
    "            \n",
    "            #Seismic loss:\n",
    "            fake_seis_unlabed = forward_model(self.generator(unlabed_batch,training=False))\n",
    "            seis_cost = self.seis_loss_fn(fake_seis_unlabed,unlabed_batch)\n",
    "            \n",
    "            g_loss = g_cost + self.alpha_weight * imp_loss + self.beta_weight * seis_cost+little_amout\n",
    "            \n",
    "        gen_gradient = tape.gradient(g_loss, self.generator.trainable_variables)\n",
    "        self.g_optimizer.apply_gradients(zip(gen_gradient, self.generator.trainable_variables))\n",
    "        \n",
    "        return {\"d_loss\": d_loss, \"g_loss\": g_loss,\"imp_loss\":(self.alpha_weight * imp_loss),\"seis_loss\":(self.beta_weight * seis_cost)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CallBack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkp_callback = keras.callbacks.ModelCheckpoint(filepath='best_model.hdf5',\n",
    "                                             monitor='imp_loss',\n",
    "                                             verbose=2,\n",
    "                                             save_best_only=True,\n",
    "                                             mode='min')\n",
    "\n",
    "earlystop_callback =  tf.keras.callbacks.EarlyStopping(monitor='imp_loss', \n",
    "            mode='min',\n",
    "            restore_best_weights=True, \n",
    "            verbose=2, \n",
    "            patience=600)\n",
    "\n",
    "nan_callback = tf.keras.callbacks.TerminateOnNaN()\n",
    "\n",
    "class PlotLosses(keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.i = 0\n",
    "        self.x = []\n",
    "        self.d_losses = []\n",
    "        self.g_losses = []\n",
    "        self.imp_losses = []\n",
    "        self.seis_losses =[]\n",
    "        \n",
    "        self.fig = plt.figure()\n",
    "        \n",
    "        self.logs = []\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        self.logs.append(logs)\n",
    "        self.x.append(self.i)\n",
    "        self.d_losses.append(logs.get('d_loss'))\n",
    "        self.g_losses.append(logs.get('g_loss'))\n",
    "        self.imp_losses.append(logs.get('imp_loss'))\n",
    "        self.seis_losses.append(logs.get('seis_loss'))\n",
    "        self.i += 1\n",
    "        \n",
    "        clear_output(wait=True) \n",
    "        if self.i>50:\n",
    "            plt.plot(self.x[-100:], self.d_losses[-100:], label=\"d_loss\")\n",
    "            plt.plot(self.x[-100:], self.g_losses[-100:], label=\"g_loss\")\n",
    "            plt.plot(self.x[-100:], self.imp_losses[-100:], label=\"imp_loss\")\n",
    "            plt.plot(self.x[-100:], self.seis_losses[-100:], label=\"seis_loss\")\n",
    "        else:\n",
    "            plt.plot(self.x, self.d_losses, label=\"d_loss\")\n",
    "            plt.plot(self.x, self.g_losses, label=\"g_loss\")\n",
    "            plt.plot(self.x, self.imp_losses, label=\"imp_loss\")\n",
    "            plt.plot(self.x, self.seis_losses, label=\"seis_loss\")\n",
    "        plt.legend()\n",
    "        plt.show();\n",
    "        \n",
    "plot_callback = PlotLosses()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the end-to-end model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_model = get_generator_model()\n",
    "#g_model.summary()\n",
    "#plot_model(g_model, show_shapes=True, show_layer_names=False)\n",
    "d_model = get_discriminator_model()\n",
    "#d_model.summary()\n",
    "#plot_model(d_model, show_shapes=True, show_layer_names=False)\n",
    "plot_losses = PlotLosses()\n",
    "\n",
    "# Instantiate the optimizer for both networks\n",
    "# (learning_rate=0.0002, beta_1=0.5 are recommended)\n",
    "generator_optimizer = keras.optimizers.Adam(learning_rate=0.0003)\n",
    "discriminator_optimizer = keras.optimizers.Adam(learning_rate = 0.0003)\n",
    "# Define the loss functions for the discriminator,\n",
    "# which should be (fake_loss - real_loss).\n",
    "# We will add the gradient penalty later to this loss function.\n",
    "\n",
    "def discriminator_loss(real_img, fake_img):\n",
    "    real_loss = tf.reduce_mean(real_img)\n",
    "    fake_loss = tf.reduce_mean(fake_img)\n",
    "    return fake_loss - real_loss\n",
    "\n",
    "# Define the loss functions for the generator.\n",
    "def generator_loss(fake_img):\n",
    "    return -tf.reduce_mean(fake_img)\n",
    "\n",
    "def impedance_loss(fake_imp,real_imp):\n",
    "    imp_mse = tf.keras.metrics.mean_squared_error(real_imp, fake_imp)\n",
    "    #imp_mse = tf.reduce_mean(tf.square(real_imp - fake_imp))\n",
    "    return imp_mse\n",
    "\n",
    "def seismic_loss(fake_seis,real_seis):\n",
    "    #fake_seis = forward_model(imp_gen)\n",
    "    #seis_loss = tf.reduce_mean(tf.square(real_seis - fake_seis))\n",
    "    seis_loss = tf.keras.metrics.mean_squared_error(real_seis, fake_seis)\n",
    "    return seis_loss\n",
    "\n",
    "# Set the number of epochs for trainining.\n",
    "epochs = 300\n",
    "\n",
    "# Instantiate the WGAN model.\n",
    "wgan = WGAN(\n",
    "    discriminator=d_model,\n",
    "    generator=g_model,\n",
    "    latent_dim=noise_dim,\n",
    "    discriminator_extra_steps=5,\n",
    "    alpha_weight = 1000,\n",
    "    beta_weight = 500,\n",
    "    gp_weight = 10\n",
    ")\n",
    "\n",
    "# Compile the WGAN model.\n",
    "wgan.compile(\n",
    "    d_optimizer=discriminator_optimizer,\n",
    "    g_optimizer=generator_optimizer,\n",
    "    g_loss_fn=generator_loss,\n",
    "    d_loss_fn=discriminator_loss,\n",
    "    imp_loss_fn = impedance_loss,\n",
    "    seis_loss_fn = seismic_loss\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start training the model.\n",
    "wgan.fit(X_train,Y_train,\n",
    "         shuffle=True,\n",
    "         batch_size=BATCH_SIZE, \n",
    "         epochs=epochs, \n",
    "         verbose = 1,\n",
    "         #callbacks=[checkp_callback]\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_predict = wgan.generator(X_test[0:7000,:,:])\n",
    "X_predict = np.transpose(X_predict[:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig1, axes1 = plt.subplots(nrows=2, ncols=1, figsize=(50,15))\n",
    "axes1[0].imshow(X_predict)\n",
    "axes1[1].imshow(np.transpose(Y_test[0:7000,:,0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trace_id = np.random.randint(X_predict.shape[1])\n",
    "plt.plot(X_predict[:,trace_id])\n",
    "\n",
    "plt.plot(Y_test[trace_id,:,0])\n",
    "plt.legend(('predicted','target'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''# serialize model to JSON\n",
    "model_json = wgan.to_json()\n",
    "with open(\"models/model0.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "wgan.save_weights(\"model/model0.h5\")\n",
    "print(\"Saved model to disk\")\n",
    "'''\n",
    "''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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